Abstract
A consensus control law is proposed for a multi-agent system of quadrotors with leader–follower communication topology for three quadrotor agents. The genetic algorithm (GA) is the proposed optimization technique to tune the consensus control parameters. The complete nonlinear model is used without any further simplifications in the simulations, while simplification in the model is used to theoretically design the controller. Different case studies and tests are done (i.e., trajectory tracking formation and switching topology) to show the effectiveness of the proposed controller. The results show good performance in all tests while achieving the consensus of the desired formations.
Highlights
In the last decade, unmanned aircrafts (UAs) have attracted researchers in different technical and scientific communities
A literature survey [2] summarized some of the potential applications of the multi-agent system, such as large object transportation, which was done in the GRASP laboratory at the University of Pennsylvania, surveillance and searching of objects, quadrotor localization in outdoor environment, which was done at the Czech Technical University, and object transportation missions [3]
From the simulation cases and the results presented, it can be concluded that the complex nonlinear model for the quadrotor system is treated as a multiple double integrator subsystem
Summary
In the last decade, unmanned aircrafts (UAs) have attracted researchers in different technical and scientific communities. UAs have a wide range of applications in various fields, such as agriculture, healthcare, and entertainment. Interest in cooperative control of multi-agent systems (MASs) has recently increased, because of its broad applications, such as fire detection [1]. There are many cooperation control problems, such as consensus control, formation control, flocking control, and so on. Another important research field is optimization, which has attracted many researchers due to the advantages provided by optimization, especially with complex and nonlinear systems. There has been much research in this field with different optimization techniques, such as genetic algorithm (GA) optimization [4,5], particle swarm optimization (PSO) [6], ant colony optimization (ACO) [7], as well as hybridization of two or more techniques [8,9]
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